We investigate a natural extension of the profiletplot of Bates and Watts to a general parametric functiong(θ) of the parameters θ in a general maximum likelihood analysis. Although the basic purpose of the extension, called the signed root deviance profile (SRDP), is to construct likelihood ratio (LR) confidence intervals forg(θ), it has various other applications that significantly extend its usefulness. The tangent to the plot of the SRDP at the maximum likelihood estimate ĝ ofg(θ) gives the linear approximation (LA) interval based on the observed information matrix. The plot may be used as a diagnostic tool to compare LA and LR intervals and to suggest transformations ofg(θ) whose LA intervals when inverted are close to the LR intervals forg(θ). The standard way to construct any profile is through repeated optimizations, but problems associated with nonlinear constraints can make this difficult. An alternative method based on integration is presented that avoids these problems. An example and a simulation study are given to illustrate the proposed methods.